Walking Fingerprinting Using Wrist Accelerometry during Activities of Daily Living in NHANES

Lily Koffman

Department of Biostatistics, Johns Hopkins School of Public Health

Introduction: accelerometry data

Introduction: accelerometry data

Introduction: big accelerometry data

Can we identify someone from their walking pattern measured by a wrist-worn accelerometer in big, free-living datasets?

Problem setup

Problem setup

Problem setup

Big picture method: time series to scalar predictors

Fingerprints summarize predictors for a given lag and are different across individuals

Fingerprints summarize predictors for a given lag and are different across individuals

Model fitting

Results in labeled datasets

32 individuals, 6 minutes of walking each

100% rank-1 accuracy (Koffman et al. 2023)

153 individuals, 3 minutes of walking each Two sessions, at least 1 week apart

Rank-1 (rank-5) % accuracies

  • Train and test on session 1
    • Logistic regression: 92 (97)
    • XGBoost: 93 (99)
  • Train on session 1, test on session 2
    • Logistic regression: 41 (75)
    • XGBoost: 58 (78)

(Koffman, Crainiceanu, and Leroux 2024)

NHANES walking identification

NHANES walking identification

NULL

Thank you!



References

Koffman, Lily, Ciprian Crainiceanu, and Andrew Leroux. 2024. “Walking Fingerprinting.” Journal of the Royal Statistical Society Series C: Applied Statistics 73 (5): 1221–41. https://doi.org/10.1093/jrsssc/qlae033.
Koffman, Lily, Yan Zhang, Jaroslaw Harezlak, Ciprian Crainiceanu, and Andrew Leroux. 2023. “Fingerprinting Walking Using Wrist-Worn Accelerometers.” Gait & Posture 103 (June): 92–98. https://doi.org/10.1016/j.gaitpost.2023.05.001.

tinyurl.com/koff-enar25